Serial and within-stage independent parallel model equivalence on the minimum completion time

1976 ◽  
Vol 14 (3) ◽  
pp. 219-238 ◽  
Author(s):  
James T. Townsend
Keyword(s):  
Completion Time ◽  
Parallel Model ◽  
Minimum Completion Time ◽  
Model Equivalence

scholarly journals Max-Min Processors Scheduling

2021 ◽  
Vol 50 (1) ◽  
pp. 5-12
Author(s):  
Hani Alquhayz ◽  
Mahdi Jemmali
Keyword(s):  
Literature Review ◽  
Knapsack Problem ◽  
Completion Time ◽  
Research Work ◽  
Parallel Processors ◽  
Experimental Results ◽  
Running Time ◽  
Similar Performance ◽  
Minimum Completion Time ◽  
Randomization Method

This paper focuses on the maximization of the minimum completion time on identical parallel processors. The objective of this maximization is to ensure fair distribution. Let a set of jobs to be assigned to several identical parallel processors. This problem is shown as NP-hard. The research work of this paper is based essentially on the comparison of the proposed heuristics with others cited in the literature review. Our heuristics are developed using essentially the randomization method and the iterative utilization of the knapsack problem to solve the studied problem. Heuristics are assessed by several instances represented in the experimental results. The results show that the knapsack based heuristic gives almost a similar performance than heuristic in a literature review but in better running time.  

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Resource Provisioning Based Scheduling Framework for Execution of Virtual Machine in Heterogeneous Environment in Cloud Computing

2020 ◽  
Vol 37 ◽  
pp. 59-68
Author(s):  
Maheta Ashish ◽  
Samrat V.O. Khanna
Keyword(s):  
Cloud Computing ◽  
System Performance ◽  
Completion Time ◽  
Resource Provisioning ◽  
Resource Manager ◽  
Cloud Resource ◽  
Cpu Utilization ◽  
Dynamic Resource ◽  
Minimum Completion Time

Cloud computing is provides resource allocation which facilitates the cloud resource provider responsible to the cloud consumers. The main objective of resource manager is to assign the dynamic resource to the task in the execution and measures response time, execution cost, resource utilization and system performance. The resource manager is optimizing the resource and measure the completion time for assign resource. The resource manager is also measure to execute the resource in the optimal way to complete the task in minimum completion time. The virtualization is techniques mandatory to allocate the dynamic resource depends on the users need. There are also green computing techniques involved for enhanced the no of server. The skewness is basically used to enhance the quality of service using the various parameters. The proposed algorithms are considered to allocate the cloud resource as per the users requirement. The advantage of proposed algorithm is to view the analysis of cpu utilization and also reduced the memory usage.

scholarly journals Project management in a competitive environment: Interdicting a CPM based project and its implications

2019 ◽  
Author(s):  
Fatih KASIMOGLU ◽  
İbrahim AKGÜN
Keyword(s):  
Project Management ◽  
Completion Time ◽  
Critical Path ◽  
Efficient Solutions ◽  
Optimal Operation ◽  
Network Interdiction ◽  
Mixed Integer ◽  
Competitive Environment ◽  
Project Completion ◽  
Minimum Completion Time

There are two opponents in a classic network interdiction problem, network owner/defender and interdictor/attacker. Each side has enough information about the other’s possible courses of action. While the network user wishes to run the network in an optimal way, the attacker with the limited resources tries to prevent the optimal operation of the network by interdicting the arcs/nodes of the network. In this study, we investigate project management in a competitive environment using a network interdiction approach. We assume that the project owner/manager strives to minimize the completion time of a Critical Path Method (CPM) based project while an opponent attempts to maximize the minimum completion time by inflicting some delays on project activities with available interdiction resources. Considering both discrete and continuous delay times, we develop two bi-level mixed-integer programming models for the interdictor. Using duality, we then convert the bi-level models to standard single-level models, which are solvable through standard optimization packages. We extend these models to find efficient solutions in terms of project completion time and interdiction resources from the interdictor’s perspective. In this respect, we develop an algorithm to find an efficient solution set for the interdictor. Next, from project manager’s standpoint, we discuss the earliest and latest scheduling times of activities in case of interdiction. Finally, we apply the developed techniques in a marketing project aiming at introducing a new product. The findings may enhance a better project management in an environment where an opponent can adversely affect the project management process by delaying some activities.

scholarly journals IMMEDIATE/BATCH MODE SCHEDULING ALGORITHMS FOR GRID COMPUTING: A REVIEW

2017 ◽  
Vol 5 (7) ◽  
pp. 1-13
Author(s):  
J.Y Maipan-uku ◽  
I Rabiu ◽  
Amit Mishra
Keyword(s):  
Execution Time ◽  
Completion Time ◽  
Computational Grid ◽  
Grid Environment ◽  
Open Problems ◽  
Batch Mode ◽  
Online Mode ◽  
On Line ◽  
Basic Concepts ◽  
Minimum Completion Time

Immediate/on-line and Batch mode heuristics are two methods used for scheduling in the computational grid environment. In the former, task is mapped onto a resource as soon as it arrives at the scheduler, while the later, tasks are not mapped onto resource as they arrive, instead they are collected into a set that is examined for mapping at prescheduled times called mapping events. This paper reviews the literature concerning Minimum Execution Time (MET) along with Minimum Completion Time (MCT) algorithms of online mode heuristics and more emphasis on Min-Min along with Max-Min algorithms of batch mode heuristics, while focusing on the details of their basic concepts, approaches, techniques, and open problems.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

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